The goal of these models is to learn a discriminative feature representation using the labels in the source domain and to map the target data to the same space through a domain-confusion loss. Thus, the feature extractor learns domain-invariant representations rather than generating models with input image distributions. With these representations, the distribution of both domains can be similar enough such that the classifier is fooled and can be directly used in the target domain even though it is trained on source samples. Therefore, whether the representations are domain-confused or not is crucial to transferring knowledge. Inspired by gan, domain confusion loss, which the discriminator produces, is introduced to improve the performance of deep da without generators. Ganin et al. [1]} introduced DANN (Domain-Adversarial Neural Network), integrating a Gradient Reversal Layer (GRL) into the standard architecture to make similar the two domains. In particular, DANN minimizes the domain confusion loss for all samples and label prediction loss only for source samples while maximizing domain confusion loss via the use of the GRL. The authors of [2]} proposed to add a domain classification layer performing binary domain classification and introduced a domain confusion loss aiming at making its outputs to be as close as possible to a uniform distribution over binary labels. Volpi et al. [3]} performed data augmentation in the source feature space exploiting a feature generator S. They obtained domain invariant features with a min-max game against generated features by S.
